{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>problem</th>\n",
       "      <th>reasoning</th>\n",
       "      <th>deepseek_solution</th>\n",
       "      <th>ground_truth_solution</th>\n",
       "      <th>correct</th>\n",
       "      <th>judge_reasoning</th>\n",
       "      <th>extracted_answer</th>\n",
       "      <th>extracted_gold</th>\n",
       "      <th>verifier_label</th>\n",
       "      <th>error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Let \\( a, b, c \\) be positive real numbers. Pr...</td>\n",
       "      <td>Okay, so I need to prove this inequality: \\(\\f...</td>\n",
       "      <td>To prove the inequality \\n\\n\\[\\n\\frac{1}{a(1+b...</td>\n",
       "      <td>1. Consider the given inequality:\\n\\n\\[\\n\\frac...</td>\n",
       "      <td>True</td>\n",
       "      <td>The provided solution correctly demonstrates t...</td>\n",
       "      <td>['1/(c*(a + 1)) + 1/(b*(c + 1)) + 1/(a*(b + 1)...</td>\n",
       "      <td>['1/(c*(a + 1)) + 1/(b*(c + 1)) + 1/(a*(b + 1)...</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A set consists of five different odd positive ...</td>\n",
       "      <td>Okay, so I need to figure out how many differe...</td>\n",
       "      <td>To determine the number of different sets of f...</td>\n",
       "      <td>\\n1. **Observe the Structure of \\( N \\)**:\\n  ...</td>\n",
       "      <td>False</td>\n",
       "      <td>The provided solution computes a total of 25 s...</td>\n",
       "      <td>['25', '25']</td>\n",
       "      <td>['24', '24']</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Given real numbers \\( a, b, c \\) and a positiv...</td>\n",
       "      <td>Okay, let's try to tackle this problem step by...</td>\n",
       "      <td>Given real numbers \\(a, b, c\\) and a positive ...</td>\n",
       "      <td>\\nWe begin by analyzing the function \\( f(x) =...</td>\n",
       "      <td>True</td>\n",
       "      <td>Both the provided solution and the ground trut...</td>\n",
       "      <td>['(3*sqrt(3))/2', '\\\\frac{3\\\\sqrt{3}}{2}']</td>\n",
       "      <td>['(3*sqrt(3))/2', '\\\\frac{3\\\\sqrt{3}}{2}']</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>In triangle $ABC$, $CA = CB$, and $D$ is the m...</td>\n",
       "      <td>Okay, so I have this geometry problem here. Le...</td>\n",
       "      <td>In triangle \\(ABC\\), \\(CA = CB\\), making it is...</td>\n",
       "      <td>1. **Identify Key Elements**: Consider \\( \\tri...</td>\n",
       "      <td>True</td>\n",
       "      <td>The provided solution correctly follows the lo...</td>\n",
       "      <td>['D', 'DE \\\\cdot DF = DA^2']</td>\n",
       "      <td>['D', 'DE \\\\cdot DF = DA^2']</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Let \\( p = 2^{3009}, q = 3^{2006}, \\) and \\( r...</td>\n",
       "      <td>Okay, so I need to figure out which of the giv...</td>\n",
       "      <td>To determine which of the given statements is ...</td>\n",
       "      <td>Given the values:\\n\\[ p = 2^{3009}, \\quad q = ...</td>\n",
       "      <td>True</td>\n",
       "      <td>The solution provided correctly follows the st...</td>\n",
       "      <td>['D', 'D']</td>\n",
       "      <td>['D', 'D']</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             problem  \\\n",
       "0  Let \\( a, b, c \\) be positive real numbers. Pr...   \n",
       "1  A set consists of five different odd positive ...   \n",
       "2  Given real numbers \\( a, b, c \\) and a positiv...   \n",
       "3  In triangle $ABC$, $CA = CB$, and $D$ is the m...   \n",
       "4  Let \\( p = 2^{3009}, q = 3^{2006}, \\) and \\( r...   \n",
       "\n",
       "                                           reasoning  \\\n",
       "0  Okay, so I need to prove this inequality: \\(\\f...   \n",
       "1  Okay, so I need to figure out how many differe...   \n",
       "2  Okay, let's try to tackle this problem step by...   \n",
       "3  Okay, so I have this geometry problem here. Le...   \n",
       "4  Okay, so I need to figure out which of the giv...   \n",
       "\n",
       "                                   deepseek_solution  \\\n",
       "0  To prove the inequality \\n\\n\\[\\n\\frac{1}{a(1+b...   \n",
       "1  To determine the number of different sets of f...   \n",
       "2  Given real numbers \\(a, b, c\\) and a positive ...   \n",
       "3  In triangle \\(ABC\\), \\(CA = CB\\), making it is...   \n",
       "4  To determine which of the given statements is ...   \n",
       "\n",
       "                               ground_truth_solution  correct  \\\n",
       "0  1. Consider the given inequality:\\n\\n\\[\\n\\frac...     True   \n",
       "1  \\n1. **Observe the Structure of \\( N \\)**:\\n  ...    False   \n",
       "2  \\nWe begin by analyzing the function \\( f(x) =...     True   \n",
       "3  1. **Identify Key Elements**: Consider \\( \\tri...     True   \n",
       "4  Given the values:\\n\\[ p = 2^{3009}, \\quad q = ...     True   \n",
       "\n",
       "                                     judge_reasoning  \\\n",
       "0  The provided solution correctly demonstrates t...   \n",
       "1  The provided solution computes a total of 25 s...   \n",
       "2  Both the provided solution and the ground trut...   \n",
       "3  The provided solution correctly follows the lo...   \n",
       "4  The solution provided correctly follows the st...   \n",
       "\n",
       "                                    extracted_answer  \\\n",
       "0  ['1/(c*(a + 1)) + 1/(b*(c + 1)) + 1/(a*(b + 1)...   \n",
       "1                                       ['25', '25']   \n",
       "2         ['(3*sqrt(3))/2', '\\\\frac{3\\\\sqrt{3}}{2}']   \n",
       "3                       ['D', 'DE \\\\cdot DF = DA^2']   \n",
       "4                                         ['D', 'D']   \n",
       "\n",
       "                                      extracted_gold  verifier_label error  \n",
       "0  ['1/(c*(a + 1)) + 1/(b*(c + 1)) + 1/(a*(b + 1)...            True        \n",
       "1                                       ['24', '24']           False        \n",
       "2         ['(3*sqrt(3))/2', '\\\\frac{3\\\\sqrt{3}}{2}']            True        \n",
       "3                       ['D', 'DE \\\\cdot DF = DA^2']            True        \n",
       "4                                         ['D', 'D']            True        "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from transformers import AutoTokenizer\n",
    "\n",
    "# Load dataset directly from Hugging Face\n",
    "math_verify_stratos = load_dataset(\"mlfoundations-dev/math_stratos_scale_verified_with_hf\", split=\"train\")\n",
    "math_verify_stratos_with_difficulty = load_dataset(\"mlfoundations-dev/math_stratos_scale_judged_and_annotated_with_difficulty\", split=\"train\")\n",
    "\n",
    "# Convert to pandas DataFrame if needed\n",
    "math_verify_stratos_df = math_verify_stratos.to_pandas()\n",
    "math_verify_stratos_with_difficulty_df = math_verify_stratos_with_difficulty.to_pandas()\n",
    "math_verify_stratos_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>problem</th>\n",
       "      <th>reasoning</th>\n",
       "      <th>deepseek_solution</th>\n",
       "      <th>ground_truth_solution</th>\n",
       "      <th>correct</th>\n",
       "      <th>judge_reasoning</th>\n",
       "      <th>extracted_answer</th>\n",
       "      <th>extracted_gold</th>\n",
       "      <th>verifier_label</th>\n",
       "      <th>error</th>\n",
       "      <th>difficulty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Let \\( a, b, c \\) be positive real numbers. Pr...</td>\n",
       "      <td>Okay, so I need to prove this inequality: \\(\\f...</td>\n",
       "      <td>To prove the inequality \\n\\n\\[\\n\\frac{1}{a(1+b...</td>\n",
       "      <td>1. Consider the given inequality:\\n\\n\\[\\n\\frac...</td>\n",
       "      <td>True</td>\n",
       "      <td>The provided solution correctly demonstrates t...</td>\n",
       "      <td>['1/(c*(a + 1)) + 1/(b*(c + 1)) + 1/(a*(b + 1)...</td>\n",
       "      <td>['1/(c*(a + 1)) + 1/(b*(c + 1)) + 1/(a*(b + 1)...</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A set consists of five different odd positive ...</td>\n",
       "      <td>Okay, so I need to figure out how many differe...</td>\n",
       "      <td>To determine the number of different sets of f...</td>\n",
       "      <td>\\n1. **Observe the Structure of \\( N \\)**:\\n  ...</td>\n",
       "      <td>False</td>\n",
       "      <td>The provided solution computes a total of 25 s...</td>\n",
       "      <td>['25', '25']</td>\n",
       "      <td>['24', '24']</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Given real numbers \\( a, b, c \\) and a positiv...</td>\n",
       "      <td>Okay, let's try to tackle this problem step by...</td>\n",
       "      <td>Given real numbers \\(a, b, c\\) and a positive ...</td>\n",
       "      <td>\\nWe begin by analyzing the function \\( f(x) =...</td>\n",
       "      <td>True</td>\n",
       "      <td>Both the provided solution and the ground trut...</td>\n",
       "      <td>['(3*sqrt(3))/2', '\\\\frac{3\\\\sqrt{3}}{2}']</td>\n",
       "      <td>['(3*sqrt(3))/2', '\\\\frac{3\\\\sqrt{3}}{2}']</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>In triangle $ABC$, $CA = CB$, and $D$ is the m...</td>\n",
       "      <td>Okay, so I have this geometry problem here. Le...</td>\n",
       "      <td>In triangle \\(ABC\\), \\(CA = CB\\), making it is...</td>\n",
       "      <td>1. **Identify Key Elements**: Consider \\( \\tri...</td>\n",
       "      <td>True</td>\n",
       "      <td>The provided solution correctly follows the lo...</td>\n",
       "      <td>['D', 'DE \\\\cdot DF = DA^2']</td>\n",
       "      <td>['D', 'DE \\\\cdot DF = DA^2']</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Let \\( p = 2^{3009}, q = 3^{2006}, \\) and \\( r...</td>\n",
       "      <td>Okay, so I need to figure out which of the giv...</td>\n",
       "      <td>To determine which of the given statements is ...</td>\n",
       "      <td>Given the values:\\n\\[ p = 2^{3009}, \\quad q = ...</td>\n",
       "      <td>True</td>\n",
       "      <td>The solution provided correctly follows the st...</td>\n",
       "      <td>['D', 'D']</td>\n",
       "      <td>['D', 'D']</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             problem  \\\n",
       "0  Let \\( a, b, c \\) be positive real numbers. Pr...   \n",
       "1  A set consists of five different odd positive ...   \n",
       "2  Given real numbers \\( a, b, c \\) and a positiv...   \n",
       "3  In triangle $ABC$, $CA = CB$, and $D$ is the m...   \n",
       "4  Let \\( p = 2^{3009}, q = 3^{2006}, \\) and \\( r...   \n",
       "\n",
       "                                           reasoning  \\\n",
       "0  Okay, so I need to prove this inequality: \\(\\f...   \n",
       "1  Okay, so I need to figure out how many differe...   \n",
       "2  Okay, let's try to tackle this problem step by...   \n",
       "3  Okay, so I have this geometry problem here. Le...   \n",
       "4  Okay, so I need to figure out which of the giv...   \n",
       "\n",
       "                                   deepseek_solution  \\\n",
       "0  To prove the inequality \\n\\n\\[\\n\\frac{1}{a(1+b...   \n",
       "1  To determine the number of different sets of f...   \n",
       "2  Given real numbers \\(a, b, c\\) and a positive ...   \n",
       "3  In triangle \\(ABC\\), \\(CA = CB\\), making it is...   \n",
       "4  To determine which of the given statements is ...   \n",
       "\n",
       "                               ground_truth_solution  correct  \\\n",
       "0  1. Consider the given inequality:\\n\\n\\[\\n\\frac...     True   \n",
       "1  \\n1. **Observe the Structure of \\( N \\)**:\\n  ...    False   \n",
       "2  \\nWe begin by analyzing the function \\( f(x) =...     True   \n",
       "3  1. **Identify Key Elements**: Consider \\( \\tri...     True   \n",
       "4  Given the values:\\n\\[ p = 2^{3009}, \\quad q = ...     True   \n",
       "\n",
       "                                     judge_reasoning  \\\n",
       "0  The provided solution correctly demonstrates t...   \n",
       "1  The provided solution computes a total of 25 s...   \n",
       "2  Both the provided solution and the ground trut...   \n",
       "3  The provided solution correctly follows the lo...   \n",
       "4  The solution provided correctly follows the st...   \n",
       "\n",
       "                                    extracted_answer  \\\n",
       "0  ['1/(c*(a + 1)) + 1/(b*(c + 1)) + 1/(a*(b + 1)...   \n",
       "1                                       ['25', '25']   \n",
       "2         ['(3*sqrt(3))/2', '\\\\frac{3\\\\sqrt{3}}{2}']   \n",
       "3                       ['D', 'DE \\\\cdot DF = DA^2']   \n",
       "4                                         ['D', 'D']   \n",
       "\n",
       "                                      extracted_gold  verifier_label error  \\\n",
       "0  ['1/(c*(a + 1)) + 1/(b*(c + 1)) + 1/(a*(b + 1)...            True         \n",
       "1                                       ['24', '24']           False         \n",
       "2         ['(3*sqrt(3))/2', '\\\\frac{3\\\\sqrt{3}}{2}']            True         \n",
       "3                       ['D', 'DE \\\\cdot DF = DA^2']            True         \n",
       "4                                         ['D', 'D']            True         \n",
       "\n",
       "   difficulty  \n",
       "0           6  \n",
       "1           7  \n",
       "2           9  \n",
       "3           8  \n",
       "4           5  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math_verify_stratos_df = math_verify_stratos_df.merge(math_verify_stratos_with_difficulty_df[[\"problem\", \"difficulty\"]], on='problem', how='inner')\n",
    "math_verify_stratos_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create confusion matrix data\n",
    "conf_matrix = math_verify_stratos_df[['verifier_label', 'correct']]\n",
    "conf_matrix = pd.crosstab(conf_matrix['verifier_label'], conf_matrix['correct'])\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(8, 6))\n",
    "im = plt.imshow(conf_matrix, cmap='Blues')\n",
    "\n",
    "# Add numbers to each cell\n",
    "for i in range(len(conf_matrix.index)):\n",
    "    for j in range(len(conf_matrix.columns)):\n",
    "        text = plt.text(j, i, conf_matrix.iloc[i, j],\n",
    "                       ha=\"center\", va=\"center\")\n",
    "\n",
    "# Add labels and title\n",
    "plt.colorbar(im)\n",
    "plt.title('Confusion Matrix: Verifier Label vs Correct')\n",
    "plt.ylabel('Math-Verify Label')\n",
    "plt.xlabel('LLM-as-a-judge Label')\n",
    "\n",
    "# Set tick labels\n",
    "plt.xticks(range(len(conf_matrix.columns)), conf_matrix.columns)\n",
    "plt.yticks(range(len(conf_matrix.index)), conf_matrix.index)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "\n",
    "def bootstrap_rates(data, n_bootstrap=1000):\n",
    "    n_samples = len(data)\n",
    "    fpr_boots = []\n",
    "    fnr_boots = []\n",
    "    \n",
    "    for _ in range(n_bootstrap):\n",
    "        # Bootstrap sample\n",
    "        boot_idx = np.random.choice(n_samples, size=n_samples, replace=True)\n",
    "        boot_sample = data.iloc[boot_idx]\n",
    "        \n",
    "        # Calculate rates for this bootstrap sample\n",
    "        TP = sum((boot_sample['verifier_label'] == True) & (boot_sample['correct'] == True))\n",
    "        TN = sum((boot_sample['verifier_label'] == False) & (boot_sample['correct'] == False))\n",
    "        FP = sum((boot_sample['verifier_label'] == False) & (boot_sample['correct'] == True))\n",
    "        FN = sum((boot_sample['verifier_label'] == True) & (boot_sample['correct'] == False))\n",
    "        \n",
    "        fpr = FP / (FP + TN) if (FP + TN) > 0 else 0\n",
    "        fnr = FN / (FN + TP) if (FN + TP) > 0 else 0\n",
    "        \n",
    "        fpr_boots.append(fpr)\n",
    "        fnr_boots.append(fnr)\n",
    "    \n",
    "    # Calculate confidence intervals\n",
    "    fpr_ci = np.percentile(fpr_boots, [2.5, 97.5])\n",
    "    fnr_ci = np.percentile(fnr_boots, [2.5, 97.5])\n",
    "    \n",
    "    return fpr_ci, fnr_ci\n",
    "\n",
    "# Calculate rates and confidence intervals for each difficulty level\n",
    "results = []\n",
    "for diff in sorted(math_verify_stratos_df['difficulty'].unique()):\n",
    "    subset = math_verify_stratos_df[math_verify_stratos_df['difficulty'] == diff]\n",
    "    \n",
    "    # Calculate point estimates\n",
    "    TP = sum((subset['verifier_label'] == True) & (subset['correct'] == True))\n",
    "    TN = sum((subset['verifier_label'] == False) & (subset['correct'] == False))\n",
    "    FP = sum((subset['verifier_label'] == False) & (subset['correct'] == True))\n",
    "    FN = sum((subset['verifier_label'] == True) & (subset['correct'] == False))\n",
    "    \n",
    "    FPR = FP / (FP + TN) if (FP + TN) > 0 else 0\n",
    "    FNR = FN / (FN + TP) if (FN + TP) > 0 else 0\n",
    "    \n",
    "    # Calculate confidence intervals\n",
    "    fpr_ci, fnr_ci = bootstrap_rates(subset)\n",
    "    \n",
    "    results.append({\n",
    "        'difficulty': diff,\n",
    "        'FPR': FPR,\n",
    "        'FNR': FNR,\n",
    "        'FPR_low': fpr_ci[0],\n",
    "        'FPR_high': fpr_ci[1],\n",
    "        'FNR_low': fnr_ci[0],\n",
    "        'FNR_high': fnr_ci[1]\n",
    "    })\n",
    "\n",
    "# Convert to DataFrame\n",
    "results_df = pd.DataFrame(results)\n",
    "\n",
    "# Create the plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Plot FPR with error bars\n",
    "plt.errorbar(results_df['difficulty'], results_df['FPR'], \n",
    "            yerr=[results_df['FPR'] - results_df['FPR_low'], \n",
    "                  results_df['FPR_high'] - results_df['FPR']],\n",
    "            fmt='bo-', label='False Positive Rate', capsize=5)\n",
    "\n",
    "# Plot FNR with error bars\n",
    "plt.errorbar(results_df['difficulty'], results_df['FNR'],\n",
    "            yerr=[results_df['FNR'] - results_df['FNR_low'],\n",
    "                  results_df['FNR_high'] - results_df['FNR']],\n",
    "            fmt='ro-', label='False Negative Rate', capsize=5)\n",
    "\n",
    "plt.xlabel('Difficulty')\n",
    "plt.ylabel('Rate')\n",
    "plt.title('False Positive and Negative Rates by Difficulty (Treating Math-Verify as Ground Truth)')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Token indices sequence length is longer than the specified maximum sequence length for this model (18112 > 512). Running this sequence through the model will result in indexing errors\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>problem</th>\n",
       "      <th>reasoning</th>\n",
       "      <th>deepseek_solution</th>\n",
       "      <th>ground_truth_solution</th>\n",
       "      <th>correct</th>\n",
       "      <th>judge_reasoning</th>\n",
       "      <th>extracted_answer</th>\n",
       "      <th>extracted_gold</th>\n",
       "      <th>verifier_label</th>\n",
       "      <th>error</th>\n",
       "      <th>num_reasoning_tokens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Let \\( a, b, c \\) be positive real numbers. Pr...</td>\n",
       "      <td>Okay, so I need to prove this inequality: \\(\\f...</td>\n",
       "      <td>To prove the inequality \\n\\n\\[\\n\\frac{1}{a(1+b...</td>\n",
       "      <td>1. Consider the given inequality:\\n\\n\\[\\n\\frac...</td>\n",
       "      <td>True</td>\n",
       "      <td>The provided solution correctly demonstrates t...</td>\n",
       "      <td>['1/(c*(a + 1)) + 1/(b*(c + 1)) + 1/(a*(b + 1)...</td>\n",
       "      <td>['1/(c*(a + 1)) + 1/(b*(c + 1)) + 1/(a*(b + 1)...</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td>18112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A set consists of five different odd positive ...</td>\n",
       "      <td>Okay, so I need to figure out how many differe...</td>\n",
       "      <td>To determine the number of different sets of f...</td>\n",
       "      <td>\\n1. **Observe the Structure of \\( N \\)**:\\n  ...</td>\n",
       "      <td>False</td>\n",
       "      <td>The provided solution computes a total of 25 s...</td>\n",
       "      <td>['25', '25']</td>\n",
       "      <td>['24', '24']</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>16361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Given real numbers \\( a, b, c \\) and a positiv...</td>\n",
       "      <td>Okay, let's try to tackle this problem step by...</td>\n",
       "      <td>Given real numbers \\(a, b, c\\) and a positive ...</td>\n",
       "      <td>\\nWe begin by analyzing the function \\( f(x) =...</td>\n",
       "      <td>True</td>\n",
       "      <td>Both the provided solution and the ground trut...</td>\n",
       "      <td>['(3*sqrt(3))/2', '\\\\frac{3\\\\sqrt{3}}{2}']</td>\n",
       "      <td>['(3*sqrt(3))/2', '\\\\frac{3\\\\sqrt{3}}{2}']</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td>4285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>In triangle $ABC$, $CA = CB$, and $D$ is the m...</td>\n",
       "      <td>Okay, so I have this geometry problem here. Le...</td>\n",
       "      <td>In triangle \\(ABC\\), \\(CA = CB\\), making it is...</td>\n",
       "      <td>1. **Identify Key Elements**: Consider \\( \\tri...</td>\n",
       "      <td>True</td>\n",
       "      <td>The provided solution correctly follows the lo...</td>\n",
       "      <td>['D', 'DE \\\\cdot DF = DA^2']</td>\n",
       "      <td>['D', 'DE \\\\cdot DF = DA^2']</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td>11040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Let \\( p = 2^{3009}, q = 3^{2006}, \\) and \\( r...</td>\n",
       "      <td>Okay, so I need to figure out which of the giv...</td>\n",
       "      <td>To determine which of the given statements is ...</td>\n",
       "      <td>Given the values:\\n\\[ p = 2^{3009}, \\quad q = ...</td>\n",
       "      <td>True</td>\n",
       "      <td>The solution provided correctly follows the st...</td>\n",
       "      <td>['D', 'D']</td>\n",
       "      <td>['D', 'D']</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td>2235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             problem  \\\n",
       "0  Let \\( a, b, c \\) be positive real numbers. Pr...   \n",
       "1  A set consists of five different odd positive ...   \n",
       "2  Given real numbers \\( a, b, c \\) and a positiv...   \n",
       "3  In triangle $ABC$, $CA = CB$, and $D$ is the m...   \n",
       "4  Let \\( p = 2^{3009}, q = 3^{2006}, \\) and \\( r...   \n",
       "\n",
       "                                           reasoning  \\\n",
       "0  Okay, so I need to prove this inequality: \\(\\f...   \n",
       "1  Okay, so I need to figure out how many differe...   \n",
       "2  Okay, let's try to tackle this problem step by...   \n",
       "3  Okay, so I have this geometry problem here. Le...   \n",
       "4  Okay, so I need to figure out which of the giv...   \n",
       "\n",
       "                                   deepseek_solution  \\\n",
       "0  To prove the inequality \\n\\n\\[\\n\\frac{1}{a(1+b...   \n",
       "1  To determine the number of different sets of f...   \n",
       "2  Given real numbers \\(a, b, c\\) and a positive ...   \n",
       "3  In triangle \\(ABC\\), \\(CA = CB\\), making it is...   \n",
       "4  To determine which of the given statements is ...   \n",
       "\n",
       "                               ground_truth_solution  correct  \\\n",
       "0  1. Consider the given inequality:\\n\\n\\[\\n\\frac...     True   \n",
       "1  \\n1. **Observe the Structure of \\( N \\)**:\\n  ...    False   \n",
       "2  \\nWe begin by analyzing the function \\( f(x) =...     True   \n",
       "3  1. **Identify Key Elements**: Consider \\( \\tri...     True   \n",
       "4  Given the values:\\n\\[ p = 2^{3009}, \\quad q = ...     True   \n",
       "\n",
       "                                     judge_reasoning  \\\n",
       "0  The provided solution correctly demonstrates t...   \n",
       "1  The provided solution computes a total of 25 s...   \n",
       "2  Both the provided solution and the ground trut...   \n",
       "3  The provided solution correctly follows the lo...   \n",
       "4  The solution provided correctly follows the st...   \n",
       "\n",
       "                                    extracted_answer  \\\n",
       "0  ['1/(c*(a + 1)) + 1/(b*(c + 1)) + 1/(a*(b + 1)...   \n",
       "1                                       ['25', '25']   \n",
       "2         ['(3*sqrt(3))/2', '\\\\frac{3\\\\sqrt{3}}{2}']   \n",
       "3                       ['D', 'DE \\\\cdot DF = DA^2']   \n",
       "4                                         ['D', 'D']   \n",
       "\n",
       "                                      extracted_gold  verifier_label error  \\\n",
       "0  ['1/(c*(a + 1)) + 1/(b*(c + 1)) + 1/(a*(b + 1)...            True         \n",
       "1                                       ['24', '24']           False         \n",
       "2         ['(3*sqrt(3))/2', '\\\\frac{3\\\\sqrt{3}}{2}']            True         \n",
       "3                       ['D', 'DE \\\\cdot DF = DA^2']            True         \n",
       "4                                         ['D', 'D']            True         \n",
       "\n",
       "   num_reasoning_tokens  \n",
       "0                 18112  \n",
       "1                 16361  \n",
       "2                  4285  \n",
       "3                 11040  \n",
       "4                  2235  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
    "\n",
    "def num_tokens(tokenizer, text):\n",
    "    encoded_text = tokenizer.encode(text)\n",
    "    num_tokens = len(encoded_text)\n",
    "    return num_tokens\n",
    "\n",
    "math_verify_stratos_df['num_reasoning_tokens'] = math_verify_stratos_df['reasoning'].apply(lambda x: num_tokens(tokenizer, x))\n",
    "math_verify_stratos_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "math_verify_stratos_df = math_verify_stratos_df.merge(math_verify_stratos_with_difficulty_df[[\"problem\", \"difficulty\"]], on='problem', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1404325/171255338.py:5: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  correct_data = [math_verify_stratos_df[math_verify_stratos_df['difficulty'] == d][math_verify_stratos_df['verifier_label'] == True]['num_reasoning_tokens'] for d in sorted(math_verify_stratos_df['difficulty'].unique())]\n",
      "/tmp/ipykernel_1404325/171255338.py:5: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  correct_data = [math_verify_stratos_df[math_verify_stratos_df['difficulty'] == d][math_verify_stratos_df['verifier_label'] == True]['num_reasoning_tokens'] for d in sorted(math_verify_stratos_df['difficulty'].unique())]\n",
      "/tmp/ipykernel_1404325/171255338.py:5: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  correct_data = [math_verify_stratos_df[math_verify_stratos_df['difficulty'] == d][math_verify_stratos_df['verifier_label'] == True]['num_reasoning_tokens'] for d in sorted(math_verify_stratos_df['difficulty'].unique())]\n",
      "/tmp/ipykernel_1404325/171255338.py:5: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  correct_data = [math_verify_stratos_df[math_verify_stratos_df['difficulty'] == d][math_verify_stratos_df['verifier_label'] == True]['num_reasoning_tokens'] for d in sorted(math_verify_stratos_df['difficulty'].unique())]\n",
      "/tmp/ipykernel_1404325/171255338.py:5: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  correct_data = [math_verify_stratos_df[math_verify_stratos_df['difficulty'] == d][math_verify_stratos_df['verifier_label'] == True]['num_reasoning_tokens'] for d in sorted(math_verify_stratos_df['difficulty'].unique())]\n",
      "/tmp/ipykernel_1404325/171255338.py:5: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  correct_data = [math_verify_stratos_df[math_verify_stratos_df['difficulty'] == d][math_verify_stratos_df['verifier_label'] == True]['num_reasoning_tokens'] for d in sorted(math_verify_stratos_df['difficulty'].unique())]\n",
      "/tmp/ipykernel_1404325/171255338.py:5: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  correct_data = [math_verify_stratos_df[math_verify_stratos_df['difficulty'] == d][math_verify_stratos_df['verifier_label'] == True]['num_reasoning_tokens'] for d in sorted(math_verify_stratos_df['difficulty'].unique())]\n",
      "/tmp/ipykernel_1404325/171255338.py:5: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  correct_data = [math_verify_stratos_df[math_verify_stratos_df['difficulty'] == d][math_verify_stratos_df['verifier_label'] == True]['num_reasoning_tokens'] for d in sorted(math_verify_stratos_df['difficulty'].unique())]\n",
      "/tmp/ipykernel_1404325/171255338.py:5: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  correct_data = [math_verify_stratos_df[math_verify_stratos_df['difficulty'] == d][math_verify_stratos_df['verifier_label'] == True]['num_reasoning_tokens'] for d in sorted(math_verify_stratos_df['difficulty'].unique())]\n",
      "/tmp/ipykernel_1404325/171255338.py:5: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  correct_data = [math_verify_stratos_df[math_verify_stratos_df['difficulty'] == d][math_verify_stratos_df['verifier_label'] == True]['num_reasoning_tokens'] for d in sorted(math_verify_stratos_df['difficulty'].unique())]\n",
      "/tmp/ipykernel_1404325/171255338.py:6: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  incorrect_data = [math_verify_stratos_df[math_verify_stratos_df['difficulty'] == d][math_verify_stratos_df['verifier_label'] == False]['num_reasoning_tokens'] for d in sorted(math_verify_stratos_df['difficulty'].unique())]\n",
      "/tmp/ipykernel_1404325/171255338.py:6: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  incorrect_data = [math_verify_stratos_df[math_verify_stratos_df['difficulty'] == d][math_verify_stratos_df['verifier_label'] == False]['num_reasoning_tokens'] for d in sorted(math_verify_stratos_df['difficulty'].unique())]\n",
      "/tmp/ipykernel_1404325/171255338.py:6: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  incorrect_data = [math_verify_stratos_df[math_verify_stratos_df['difficulty'] == d][math_verify_stratos_df['verifier_label'] == False]['num_reasoning_tokens'] for d in sorted(math_verify_stratos_df['difficulty'].unique())]\n",
      "/tmp/ipykernel_1404325/171255338.py:6: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  incorrect_data = [math_verify_stratos_df[math_verify_stratos_df['difficulty'] == d][math_verify_stratos_df['verifier_label'] == False]['num_reasoning_tokens'] for d in sorted(math_verify_stratos_df['difficulty'].unique())]\n",
      "/tmp/ipykernel_1404325/171255338.py:6: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  incorrect_data = [math_verify_stratos_df[math_verify_stratos_df['difficulty'] == d][math_verify_stratos_df['verifier_label'] == False]['num_reasoning_tokens'] for d in sorted(math_verify_stratos_df['difficulty'].unique())]\n",
      "/tmp/ipykernel_1404325/171255338.py:6: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  incorrect_data = [math_verify_stratos_df[math_verify_stratos_df['difficulty'] == d][math_verify_stratos_df['verifier_label'] == False]['num_reasoning_tokens'] for d in sorted(math_verify_stratos_df['difficulty'].unique())]\n",
      "/tmp/ipykernel_1404325/171255338.py:6: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  incorrect_data = [math_verify_stratos_df[math_verify_stratos_df['difficulty'] == d][math_verify_stratos_df['verifier_label'] == False]['num_reasoning_tokens'] for d in sorted(math_verify_stratos_df['difficulty'].unique())]\n",
      "/tmp/ipykernel_1404325/171255338.py:6: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  incorrect_data = [math_verify_stratos_df[math_verify_stratos_df['difficulty'] == d][math_verify_stratos_df['verifier_label'] == False]['num_reasoning_tokens'] for d in sorted(math_verify_stratos_df['difficulty'].unique())]\n",
      "/tmp/ipykernel_1404325/171255338.py:6: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  incorrect_data = [math_verify_stratos_df[math_verify_stratos_df['difficulty'] == d][math_verify_stratos_df['verifier_label'] == False]['num_reasoning_tokens'] for d in sorted(math_verify_stratos_df['difficulty'].unique())]\n",
      "/tmp/ipykernel_1404325/171255338.py:6: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  incorrect_data = [math_verify_stratos_df[math_verify_stratos_df['difficulty'] == d][math_verify_stratos_df['verifier_label'] == False]['num_reasoning_tokens'] for d in sorted(math_verify_stratos_df['difficulty'].unique())]\n",
      "/tmp/ipykernel_1404325/171255338.py:12: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n",
      "  plt.boxplot(correct_data, positions=positions-width/2, patch_artist=True,\n",
      "/tmp/ipykernel_1404325/171255338.py:18: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n",
      "  plt.boxplot(incorrect_data, positions=positions+width/2, patch_artist=True,\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the number of reasoning tokens vs difficulty as a boxplot\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Create separate box plots for correct and incorrect answers\n",
    "correct_data = [math_verify_stratos_df[math_verify_stratos_df['difficulty'] == d][math_verify_stratos_df['verifier_label'] == True]['num_reasoning_tokens'] for d in sorted(math_verify_stratos_df['difficulty'].unique())]\n",
    "incorrect_data = [math_verify_stratos_df[math_verify_stratos_df['difficulty'] == d][math_verify_stratos_df['verifier_label'] == False]['num_reasoning_tokens'] for d in sorted(math_verify_stratos_df['difficulty'].unique())]\n",
    "\n",
    "# Plot box plots side by side\n",
    "positions = np.arange(len(correct_data))\n",
    "width = 0.35\n",
    "\n",
    "plt.boxplot(correct_data, positions=positions-width/2, patch_artist=True,\n",
    "           boxprops=dict(facecolor='lightgreen', color='black'),\n",
    "           medianprops=dict(color='black'),\n",
    "           flierprops=dict(markerfacecolor='lightgreen'),\n",
    "           labels=[''] * len(correct_data))\n",
    "\n",
    "plt.boxplot(incorrect_data, positions=positions+width/2, patch_artist=True,\n",
    "           boxprops=dict(facecolor='lightcoral', color='black'),\n",
    "           medianprops=dict(color='black'),\n",
    "           flierprops=dict(markerfacecolor='lightcoral'),\n",
    "           labels=sorted(math_verify_stratos_df['difficulty'].unique()))\n",
    "\n",
    "plt.xlabel('Difficulty')\n",
    "plt.ylabel('Number of Reasoning Tokens')\n",
    "plt.title('Number of Reasoning Tokens vs Difficulty')\n",
    "\n",
    "# Add legend\n",
    "from matplotlib.patches import Patch\n",
    "legend_elements = [Patch(facecolor='lightgreen', label='Correct'),\n",
    "                  Patch(facecolor='lightcoral', label='Incorrect')]\n",
    "plt.legend(handles=legend_elements)\n",
    "\n",
    "plt.grid(True, axis='y')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dcft_dev",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
